Clinical lipidomics in the era of the big data.

Lipidomics as a branch of metabolomics provides unique information on the complex lipid profile in biological materials. In clinically focused studies, hundreds of lipids together with available clinical information proved to be an effective tool in the discovery of biomarkers and understanding of pathobiochemistry. However, despite the introduction of lipidomics nearly twenty years ago, only dozens of big data studies using clinical lipidomics have been published to date. In this review, we discuss the lipidomics workflow, statistical tools, and the challenges of standartisation. The consequent summary divided into major clinical areas of cardiovascular disease, cancer, diabetes mellitus, neurodegenerative and liver diseases is demonstrating the importance of clinical lipidomics. In these publications, the potential of lipidomics for prediction, diagnosis or finding new targets for the treatment of selected diseases can be seen. The first of these results have already been implemented in clinical practice in the field of cardiovascular diseases, while in other areas we can expect the application of the results summarized in this review in the near future.

Lipidomic and metabolomic analysis reveals changes in biochemical pathways for non-small cell lung cancer tissues.

Lung cancer represents one of the leading worldwide causes of cancer death, but the pathobiochemistry of this disease is still not fully understood. Here we characterize the lipidomic and metabolomic profiles of the tumor and surrounding normal tissues for 23 patients with non-small cell lung cancer. In total, 500 molecular species were identified and quantified by a combination of the lipidomic shotgun tandem mass spectrometry (MS/MS) analysis and the targeted metabolomic approach using liquid chromatography (LC) - MS/MS. The statistical evaluation includes multivariate and univariate methods with the emphasis on paired statistical approaches. Our research revealed significant changes in several biochemical pathways related to the central carbon metabolism, acylcarnitines, dipeptides as well as the disruption in the lipid metabolism observed mainly for glycerophospholipids, sphingolipids, and cholesteryl esters.

Mass spectrometric analysis of purine de novo biosynthesis intermediates.

Purines are essential molecules for all forms of life. In addition to constituting a backbone of DNA and RNA, purines play roles in many metabolic pathways, such as energy utilization, regulation of enzyme activity, and cell signaling. The supply of purines is provided by two pathways: the salvage pathway and de novo synthesis. Although purine de novo synthesis (PDNS) activity varies during the cell cycle, this pathway represents an important source of purines, especially for rapidly dividing cells. A method for the detailed study of PDNS is lacking for analytical reasons (sensitivity) and because of the commercial unavailability of the compounds. The aim was to fully describe the mass spectrometric fragmentation behavior of newly synthesized PDNS-related metabolites and develop an analytical method. Except for four initial ribotide PDNS intermediates that preferentially lost water or phosphate or cleaved the forming base of the purine ring, all the other metabolites studied cleaved the glycosidic bond in the first fragmentation stage. Fragmentation was possible in the third to sixth stages. A liquid chromatography-high-resolution mass spectrometric method was developed and applied in the analysis of CRISPR-Cas9 genome-edited HeLa cells deficient in the individual enzymatic steps of PDNS and the salvage pathway. The identities of the newly synthesized intermediates of PDNS were confirmed by comparing the fragmentation patterns of the synthesized metabolites with those produced by cells (formed under pathological conditions of known and theoretically possible defects of PDNS). The use of stable isotope incorporation allowed the confirmation of fragmentation mechanisms and provided data for future fluxomic experiments. This method may find uses in the diagnosis of PDNS disorders, the investigation of purinosome formation, cancer research, enzyme inhibition studies, and other applications.